Forward Consistency Learning with Gated Context Aggregation for Video Anomaly Detection

TL;DR

FoGA is a lightweight, real-time video anomaly detection model that uses forward consistency learning and gated context aggregation to improve accuracy while maintaining efficiency suitable for edge devices.

Contribution

The paper introduces FoGA, a novel lightweight VAD model with a gated context aggregation module and forward consistency loss, enabling efficient and accurate anomaly detection on resource-limited devices.

Findings

Outperforms state-of-the-art methods in accuracy.

Runs up to 155 FPS on standard hardware.

Achieves a good balance between performance and efficiency.

Abstract

As a crucial element of public security, video anomaly detection (VAD) aims to measure deviations from normal patterns for various events in real-time surveillance systems. However, most existing VAD methods rely on large-scale models to pursue extreme accuracy, limiting their feasibility on resource-limited edge devices. Moreover, mainstream prediction-based VAD detects anomalies using only single-frame future prediction errors, overlooking the richer constraints from longer-term temporal forward information. In this paper, we introduce FoGA, a lightweight VAD model that performs Forward consistency learning with Gated context Aggregation, containing about 2M parameters and tailored for potential edge devices. Specifically, we propose a Unet-based method that performs feature extraction on consecutive frames to generate both immediate and forward predictions. Then, we introduce a gated context aggregation module into the skip connections to dynamically fuse encoder and decoder features at the same spatial scale. Finally, the model is jointly optimized with a novel forward consistency loss, and a hybrid anomaly measurement strategy is adopted to integrate errors from both immediate and forward frames for more accurate detection. Extensive experiments demonstrate the effectiveness of the proposed method, which substantially outperforms state-of-the-art competing methods, running up to 155 FPS. Hence, our FoGA achieves an excellent trade-off between performance and the efficiency metric.